Recognition of erosion risk areas using Neural Network Technology: an application to the Island of Corfu

Abstract

There is a wide range of alternative approaches to study erosion processes. In this paper, we describe the construction of a model based on the interaction of Geographical Information System (GIS) and Artificial Neural Networks (ANN). The neural model uses supervised competitive learning process. The whole process begins with the digitization of collected data and the definition of the input variables, such as slope form and gradient, susceptibility to erosion and protective cover. The input variables are transformed into the erosion risk output variable using the neural model. The last stage is the development of a map of erosion risk zones. As a case study the island of Corfu (Ionian Sea, Greece) was chosen, which consists of lithologies very vulnerable to erosion and receives considerable amounts of rainfall, especially in comparison to the rest of Greece. Finally, the whole model was validated and its proper function was confirmed by field data observations.